Macro-/Micro-Design of Electrochemical Energy Battery Based on Machine Learning

被引：0
|
作者：
Li J. [1 ]
Cai J. [1 ]
Han Y. [1 ]
Wang Z. [1 ]
Chen A. [1 ]
Ye S. [1 ]
机构：
[1] Shanghai Jiao Tong University and Electrical Engineering, Key Laboratory of Film and Microfabrication, Ministry of Education, Shanghai
来源：
Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society | 2023年 / 51卷 / 02期
关键词：
battery materials; battery state; data mining; machine learning;
D O I：
10.14062/j.issn.0454-5648.20220639
中图分类号：
学科分类号：
摘要：
The energy storage systems are an important basis for electric vehicles and electronic devices. The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure, material properties, and battery macroscopic properties. This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries. The data sources of machine learning battery design, advantages and disadvantages of algorithms and their application scenarios in the battery field, related innovative work in recent years and their prospects were discussed. This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems. © 2023 Chinese Ceramic Society. All rights reserved.
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页码：438 / 451
页数：13
相关论文
共 115 条
  • [1] KOOHI-FAYEGH S, ROSEN M A., A review of energy storage types, applications and recent developments, J Energy Storage, 2, (2020)
  • [2] CHEN C, ZUO Y, YE W, Et al., A critical review of machine learning of energy materials, Adv Energy Mater, 10, 8, (2020)
  • [3] BACHMAN J C, MUY S, GRIMAUD A, Et al., Inorganic solid-state electrolytes for lithium batteries: Mechanisms and properties governing ion conduction, Chem Rev, 116, 1, pp. 140-162, (2016)
  • [4] FU Z H, CHEN X, ZHANG Q., Review on the lithium transport mechanism in solid-state battery materials, WIREs Comput Molecular Sci, (2022)
  • [5] WANG Z, ZHENG X, CHEN A, Et al., Unraveling the anchoring effect of mxene-supported single atoms as cathodes for aluminum–sulfur batteries[J], ACS Mater Lett, 4, 8, pp. 1436-1445, (2022)
  • [6] LU C, FANG R, CHEN X., Single-atom catalytic materials for advanced battery systems, Adv Mater, 32, 16, (2020)
  • [7] LI M, LU Jun, JI Xiulei, Et al., Design strategies for nonaqueous multivalent-ion and monovalent-ion battery anodes, Nat Rev Mater, 5, 4, (2020)
  • [8] LIU Y, GUO B, ZOU X, Et al., Machine learning assisted materials design and discovery for rechargeable batteries[J], Energy Storage Mater, 31, pp. 434-450, (2020)
  • [9] LIOW C H, KANG Hyeonmuk, KIM Seunggu, Et al., Machine learning assisted synthesis of lithium-ion batteries cathode materials, Nano Energy, 98, (2022)
  • [10] KNOSGAARD N R, THYGESEN K S., Representing individual electronic states for machine learning GW band structures of 2D materials, Nat Commun, 13, 1, (2022)
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